The Data Warehouse

1
Chapter 13

• The Data Warehouse
• Database Systems Design, Implementation, and
  Management, Seventh Edition, Rob and Coronel

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Relational OLAP (continued)
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Multidimensional OLAP

• Extends OLAP functionality to multidimensional
  database management systems (MDBMSs)
• MDBMS end users visualize stored data as a 3D
  cube-a data cube
• Data cubes can grow to n number of dimensions,
  becoming hypercubes
• To speed access, data cubes are held in memory in
  a cube cache

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Multidimensional OLAP (continued)
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Relational vs. Multidimensional OLAP
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Star Schemas

• Data modeling technique used to map
  multidimensional decision support data into
  relational database
• Creates near equivalent of multidimensional
  database schema from existing relational database
• Yield an easily implemented model for
  multidimensional data analysis, while still
  preserving relational structures on which
  operational database is built
• Has four components facts, dimensions,
  attributes, and attribute hierarchies

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Facts

• Numeric measurements (values) that represent
  specific business aspect or activity
• Normally stored in fact table that is center of
  star schema
• Fact table contains facts that are linked through
  their dimensions
• Metrics are facts computed or derived at run time

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Dimensions
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Attributes

• Used to search, filter, or classify facts
• Dimensions provide descriptive characteristics
  about the facts through their attributes

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Attributes (continued)
11
Attributes (continued)
12
Attributes (continued)

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Attributes (continued)
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Attribute Hierarchies

• Provides top-down data organization
• Provides capability to perform drill-down and
  roll-up searches in a data warehouse

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Attribute Hierarchies (continued)
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Attribute Hierarchies (continued)
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Star Schema Representation

• Each dimension record is related to thousands of
  fact records
• Facilitates data retrieval functions

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Star Schema Representation (continued)